Fuzzy Filter Research Articles

A Hybrid Fuzzy based Cross Neighbor Filtering (HF-CNF) for Image Enhancement of fine and coarse powder Scanned Electron Microscopy (SEM) images

Image enhancement is one of the most critical stages towards any image processing application. The outcome of image enhancement determines the accuracy and precise nature of the overall output from the image processing under interest. This research paper has shown specific interests towards enhancement of Scanned Electron Microscopic (SEM) images which are primarily concerned with projection of fine details exist in internal details of surfaces, metals, fine powders, fibers etc. These fine details play a dominant role in detection of minute cracks, artifacts, progressing faults, texture of powders, their coarseness or fineness, internal details of fibers in forensics. However, due to the image capturing process which is through conventional camera-based models, noise tends to be a major source in degrading or blurring the underlying vital information. A cross neighbor fuzzy filter is a hybrid combination called Hybrid Fuzzy Based Cross Neighbor Filtering (HF-CNF) which is proposed in this research paper in order to minimize impulse and random noise to a great extent also to fine tune the further processing stages. The proposed method has been subjected to extensive analysis by comparison with state of art and recent benchmark methods and superior performance justified in terms of several validation metrics.

Generalized Fuzzy Filters in Quantales and Their Approximations

The present paper represents the behaviour of fuzzy filters and (α, β)-fuzzy filters in Quantale. The detailed study of relationship among crisp filter, fuzzy filters and (α, β)-fuzzy filters in quantale are discussed. An important part is played by quantale homomorphism which shows inverse image of (∈, ∈ ∨q)-fuzzy filter is again (∈, ∈ ∨q)-fuzzy filter. Under (α, β)-fuzzy map, it is seen that inverse image of (α, β)-fuzzy filter is again a fuzzy filter under quantale homomorphism. The relationship between fuzzy filter and (∈γ, ∈γ ∨qδ)-fuzzy filters are also discussed. Further, generalized approximation of fuzzy filter, (∈, ∈ ∨q)-fuzzy filter and (∈γ, ∈γ ∨qδ)-fuzzy filter are discussed.

Study strong Sheffer stroke non-associative MV-algebras by fuzzy filters

In this paper, some types of fuzzy filters of a strong Sheffer stroke non-associative MV-algebra (for short, strong Sheffer stroke NMV-algebra) are introduced. By presenting new properties of filters, we define a prime filter in this algebraic structure. Then (prime) fuzzy filters of a strong Sheffer stroke NMV-algebra are determined and some features are proved. Finally, we built quotient strong Sheffer stroke NMV-algebra by a fuzzy filter.

Fuzzy filters of IL-algebras

Fuzzy filters of IL-algebras

A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images.

In this paper, we present rule-based fuzzy inference systems that consist of a series of mathematical representations based on fuzzy concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms. In particular, in most practical applications (i.e., biomedical image analysis), the emphasis is placed primarily on fuzzy filtering algorithms, with the main advantages of restoration of corrupted medical images and the interpretation capability, along with the capability of edge preservation and relevant image information for accurate diagnosis of diseases.

An efficient fuzzy deep learning approach to recognize 2D faces using FADF and ResNet-164 architecture

Deep learning using fuzzy is highly modular and more accurate. Adaptive Fuzzy Anisotropy diffusion filter (FADF) is used to remove noise from the image while preserving edges, lines and improve smoothing effects. By detecting edge and noise information through pre-edge detection using fuzzy contrast enhancement, post-edge detection using fuzzy morphological gradient filter and noise detection technique. Convolution Neural Network (CNN) ResNet-164 architecture is used for automatic feature extraction. The resultant feature vectors are classified using ANFIS deep learning. Top-1 error rate is reduced from 21.43% to 18.8%. Top-5 error rate is reduced to 2.68%. The proposed work results in high accuracy rate with low computation cost. The recognition rate of 99.18% and accuracy of 98.24% is achieved on standard dataset. Compared to the existing techniques the proposed work outperforms in all aspects. Experimental results provide better result than the existing techniques on FACES 94, Feret, Yale-B, CMU-PIE, JAFFE dataset and other state-of-art dataset.

Design of Knitted Garment Design Model Based on Mathematical Image Theory

Iconology is an approach used by Western art theorists and art history researchers to explain plastic art and even relative to other various plastic art activities. This article is aimed at researching and discussing the design of knitted garment design model based on mathematical image theory. This article first discusses the application of digital image processing in the field of knitted clothing; digital image processing technology has a wide range of applications in the field of knitted clothing, including four aspects of fiber, yarn, fabric, and clothing, and proposes digital image filtering technology and digital image fuzzy filtering algorithm and then researches and analyzes the drape performance of knitted fabrics. Fabric drape is an important performance that reflects the beauty of the appearance of the fabric. The design of knitted garments is discussed based on mathematical imagery, and finally, the pressure performance of knitted garments is tested and analyzed. The experimental results in this paper show that the data correlation of the clothing pressure measured in different situations of the upper body is 0.87328, 0.779832, and 0.780213, respectively, under the plain weave, rib, and mesh fabric clothing fabrics. It shows that under the pressure of this degree of knitted clothing, it is in line with the needs of human healthy growth, because a certain degree of clothing pressure can maintain human physical and mental health and improve the quality of life.

Distributed H∞ fuzzy filtering for nonlinear systems interconnected over graphs

SummaryThis article investigates the distributed fuzzy filtering problem for a class of Takagi–Sugeno (T‐S) fuzzy model‐based nonlinear systems interconnected over an undirected graph. The system we consider consists of numbers of heterogeneous nonlinear sub‐units interconnected over an undirected graph by sensing, computing, and communicating with each other. First, the system is represented by an undirected graph, a T‐S fuzzy model‐based state‐space equation of each subsystem and an interconnection condition. Based on this model, the concepts of the well‐posedness, stability, and contractiveness for the class of systems are introduced, and the distributed fuzzy filtering problem is established. By applying membership‐dependent multi‐Lyapunov functions, a sufficient condition on the well‐posedness, stability, and contractiveness of the open‐loop plant is then derived in terms of linear matrix inequalities (LMIs). And then, a distributed fuzzy filter inheriting the interconnected structure of the plant is designed such that the filtering error system is contractive, and a sufficient condition is correspondingly given to obtain the desired fuzzy filter parameters by solving a set of LMIs. Finally, a numerical simulation is exploited to show the validity of the proposed design method.

Fuzzy filter cost-sensitive feature selection with differential evolution

In recent years, a variety of feature selection methods based on evolutionary computation (EC) techniques have been developed for classification due to their robustness and search ability. However, the previous EC-based feature selection research mostly focuses on enhancing the prediction accuracy without taking the costs associated with the learning process into consideration. In other words, the impact of using EC-based feature selection to improve the cost-sensitive classification performance has not been well studied. Further, it is not possible to find a EC-based filter cost-sensitive feature selection method in the literature, to the best of our knowledge. We therefore design a cost-sensitive evaluation criterion using the principles of fuzzy mutual estimator and then adopt the criterion in differential evolution framework. According to a variety of experiments conducted on various benchmarks, the proposed filter method can effectively minimize both the classification error rate and the feature cost by removing irrelevant and distracting features from the dataset in a reasonable time.

The fuzzy filter based on the method of areas’ ratio

The paper describes a new fuzzy filter based on the method of areas’ ratio which allows to reduce noise during filtering signals. To expand the functionality of the method of areas’ ratio two computational procedures were developed to eliminate errors inherent in classical defuzzification models, namely a narrow range of defuzzification and insensitivity of a defuzzification model. Presented various computational procedures for the fuzzy filter can change the properties of the output variable resulting. As an example, the proposed mathematical model of the Fuzzy Filter based on the Method of Areas’ Ratio illustrated its distinctive characteristics are shown. Firstly, the Fuzzy Filter based on the Method of Areas’ Ratio model has the property of continuity. Secondly, computational procedures provide an increase in the performance of the fuzzy filter. Using detailed numerically calculated Root Mean Square Error and Mean Absolute Percentage Error evaluated the proposed model of the fuzzy filter with other filters such as Kalman Filter, Fuzzy Kalman Filter, Ensemble Kalman Filter and Fuzzy Extended Kalman Filter, Basic defuzzification distributions, Fuzzy mean, Quality method, Root mean square and New weighted trapezoid median average. One of the main goals of the article was to confirm the hypothesis about the possibility of using a fuzzy filter based on the method of area’s ratio for filtering signals. As well as studies of the sensitivity of the proposed fuzzy filter is based on the Root Mean Square Error and Mean Absolute Percentage Error coefficients. These coefficients were established during the experimental studies and showed that the sensitivity of the fuzzy filter based on the method of area’s ratio is better than other filters.

Properties of <i>R<sub>0</sub></i>-algebra based on hesitant fuzzy MP filters and congruence relations

<abstract><p>The hesitant fuzzy MP filter and the hesitant fuzzy congruence relation of algebra are introduced in this study, and their properties are investigated. The comparable characterization of a hesitant fuzzy MP filter is then provided. Furthermore, we established that the set of all hesitant fuzzy congruence relations and the set of all hesitant fuzzy MP filters of <italic>R<sub>0</sub></italic>-algebra are complete lattice isomorphism based on the features of the hesitant fuzzy congruence relation in <italic>R<sub>0</sub></italic>-algebra.</p></abstract>

Properties of <i>R<sub>0</sub></i>-algebra based on hesitant fuzzy MP filters and congruence relations

<abstract><p>The hesitant fuzzy MP filter and the hesitant fuzzy congruence relation of algebra are introduced in this study, and their properties are investigated. The comparable characterization of a hesitant fuzzy MP filter is then provided. Furthermore, we established that the set of all hesitant fuzzy congruence relations and the set of all hesitant fuzzy MP filters of <italic>R<sub>0</sub></italic>-algebra are complete lattice isomorphism based on the features of the hesitant fuzzy congruence relation in <italic>R<sub>0</sub></italic>-algebra.</p></abstract>

De-Speckling of SAR Images with Fuzzy Filters along with Altered Preserved Edge Values

In this research, the denoising of speckled SAR image has been done with fuzzy filters (ATMED, TMED, ATMAV & TMAV). SAR image or Synthetic Aperture Radar image consists of the informatics of ISW (Internal solitary waves). A new technique has been proposed which preserved the edge pixels by fuzzy edge detection method and then altered with the filtered image-pixels by fuzzy filtration for getting the denoised image. The comparative result shows that the proposed filter performs better than the other filtered results in terms of PSNR (41.61 dB), MAE (1.47), MSE (4.54) for TMAVxAPE & SSIM (81%) for ATMEDwAPE. The proposed method in this research shows better SSI (Spackle Suppression Index) value. Therefore the experimental result illustrates that the suggested fuzzy filter is much more capable of simultaneously protecting edges and suppressing speckle noise. This research will be beneficial to remove spackle noise from SAR images and can be used for remote sensing and mapping of surface area of earth.

Closure Fuzzy Filters of Decomposable MS-Algebras

Abstract In this paper, we give some results of closure fuzzy filters of decomposable MS-algebras, characterization of closure fuzzy filters, and homomorphism of closure fuzzy filters. It is observed that the class of all closure fuzzy filters of a decomposable MS-algebra forms a bounded distributive lattice. We have also characterized the class of all closure fuzzy filters of a decopmposable MS-algebra in terms of boosters. Some properties of the homomorphic images and the inverse images of closure fuzzy filters are studied.

Improved filtering H∞ finite frequency of Takagi-Sugeno fuzzy systems

The work treats the filter H∞ finite frequency (FF) in Takagi-Sugeno (T-S) two dimensional (2-D) systems described by Fornasini-Marchesini local state-space (FM LSS)models. The goal of this work is to find an FF H∞ T-S fuzzy filter model design in such a way that the error system is stable and has a reduced FF H∞ performance over FF area swith noise is established as aprerequisite. Via the use of the generalized Kalman Yakubovich Popov (gKYP) lemma, Lyapunov functions approach, Finsler’s lemma, and parameterize slack matrices, new design conditions guaranteeing the FF H∞ T-S fuzzy filter method of FM LSS models are developed by solving linear matrix inequalities (LMIs). At last, the simulation results are provided to show the effectiveness and the validity of the proposed FF T-S fuzzy of FM LSS models strategy by a practical application has been made.

Slopping index for LD converters based on sound and image data fusion by fuzzy Kalman filter

ABSTRACT The LD (Linz Donawitz) steelmaking process is the most used in the steel industry due to its high-volume capacity and low cost per ton of steel produced. However, the basic oxygen steelmaking process in LD converters is subjected to potential steel charge overflows, often called ‘slopping’. Besides yield losses, slopping events can damage the environment and expose employees to danger. More than ever, steelmaking plants need to avoid this type of event to keep producing as environmental impacts are no more tolerable by society. Steelmaking plants already use different methods to monitor and detect slopping events, but they are often limited and unreliable. Therefore, this paper proposes a multi-sensor data fusion process to generate a reliable slopping index to warn operators of potential slopping events and detect the triggered ones. The work is based on sound and image data (67 heats with 27 slopping events) collected on previous trials at a 350-ton LD converter. The Kalman filter was applied as a data fusion agent of two indexes, one resulted from computer vision analysis of the LD converter mouth (image data), the other resulted from digital signal analysis of sound captured on the converter’s hood (sound data). Fuzzy sets were applied for adaptative tuning of the Kalman filter to improve the data fusion process. Besides the increase of alarm accuracy and heat classification, the data fusion index worked better on different scenarios and produced a more reliable indicator for a slopping prevention system.

FUZZY MEDIAL FILTERS OF PSEUDO BE-ALGEBRAS

In this paper, the notion of fuzzy medial filters of a pseudo BE-algebra is defined, and some of the properties are investigated. We show that the set of all fuzzy medial filters of a pseudo BE-algebra is a complete lattice. Moreover, we state that in commutative pseudo BE-algebras fuzzy filters and fuzzy medial filters coincide. Finally, the notion of a fuzzy implicative filter is introduced and proved that every fuzzy implicative filter is a fuzzy medial filter, and we show that the converse is not valid in general.

Event-Driven Fault Detection for Discrete-Time Interval Type-2 Fuzzy Systems

This article considers the event-driven fault detection (FD) problem for discrete-time interval type-2 (IT2) fuzzy networked control systems (NCSs) with nonlinear perturbations. To reduce signal transmission, an event-driven mechanism in the NCSs is introduced. A novel FD fuzzy filter is designed for generating a residual signal and detecting actuator faults by considering the event-driven strategy, nonlinear perturbations, communication delay, and mismatched membership functions. It is shown that the proposed event-driven FD technique is effective to optimize performance compared with the existing FD strategy. Finally, two examples are presented to verify the advantages of the novel design technique.

UAV navigation based on adaptive fuzzy backstepping controller using visual odometry

ABSTRACT This study investigates the 3D trajectory tracking of a quadrotor-type unmanned aerial vehicle (UAV) using a visual localization system integrated in the feedback control loop. First, adaptive stereo visual odometry (SVO) is proposed to solve the UAV localization problem. The proposed solution mainly employs adaptive iterative closest point speeded-up robust features. Second, a vision-based trajectory tracking algorithm is implemented using a backstepping controller, whose parameters are optimized using the particle swarm optimization algorithm. Finally, to avoid the limitations of visual localization (e.g. dark environment, uniform area, and dynamic scene), the visual pose is supported by the inertial pose obtained using a fuzzy adaptive Kalman filter (FAKF). Based on the number and average depth of the estimated 3D points, the FAKF algorithm adaptively tunes the extended Kalman filter parameters. The proposed algorithms are validated using simulation and experimental data. Many scenarios are considered with different trajectories. Good performance is achieved, confirming the efficiency of the proposed approach.

Optimized Hybrid Model for Gaussian Noise Reduction images

In this paper, image noise is removed by using a hybrid model of wiener and fuzzy filters. It is a challenging task to remove Gaussian noise (GN) from an image and to protect the image’s edges. The Fuzzy-Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results proved that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques.